package com.dkd.manage.task.allocator.algorithm;

import lombok.extern.slf4j.Slf4j;

import java.util.Random;

@Slf4j
public class NeuralNetworkPredictor {
    private static NeuralNetworkPredictor instance;
    private Random random = new Random();

    // 简化的神经网络权重
    private double[][] weights = {
            {0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8},
            {0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9},
            {0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.0}
    };

    private NeuralNetworkPredictor() {
        // 私有构造函数
    }

    public static NeuralNetworkPredictor getInstance() {
        if (instance == null) {
            instance = new NeuralNetworkPredictor();
        }
        return instance;
    }

    public double predict(double[] features) {
        try {
            log.debug("神经网络预测，输入特征维度: {}", features.length);

            if (features.length != weights[0].length) {
                log.warn("特征维度不匹配，期望: {}, 实际: {}", weights[0].length, features.length);
                return 5.0; // 默认分数
            }

            // 输入层到隐藏层
            double[] hiddenLayer = new double[weights.length];
            for (int i = 0; i < weights.length; i++) {
                hiddenLayer[i] = 0.0;
                for (int j = 0; j < features.length; j++) {
                    hiddenLayer[i] += features[j] * weights[i][j];
                }
                hiddenLayer[i] = relu(hiddenLayer[i]); // ReLU激活函数
            }

            // 隐藏层到输出层（简化）
            double output = 0.0;
            for (double hiddenValue : hiddenLayer) {
                output += hiddenValue;
            }
            output = sigmoid(output / hiddenLayer.length) * 10; // 归一化到0-10分

            log.debug("神经网络预测完成，输出: {}", output);
            return output;

        } catch (Exception e) {
            log.error("神经网络预测异常: {}", e.getMessage());
            return 5.0; // 默认分数
        }
    }

    private double sigmoid(double x) {
        return 1.0 / (1.0 + Math.exp(-x));
    }

    private double relu(double x) {
        return Math.max(0, x);
    }

    // 训练方法（简化）
    public void train(double[][] trainingData, double[] labels, int epochs) {
        log.info("开始神经网络训练，数据量: {}, 轮次: {}", trainingData.length, epochs);
        // 简化的训练逻辑
        for (int epoch = 0; epoch < epochs; epoch++) {
            // 模拟训练过程
            for (int i = 0; i < trainingData.length; i++) {
                double prediction = predict(trainingData[i]);
                double error = labels[i] - prediction;

                // 简化的权重更新
                for (int j = 0; j < weights.length; j++) {
                    for (int k = 0; k < weights[j].length; k++) {
                        weights[j][k] += 0.01 * error * trainingData[i][k];
                    }
                }
            }
        }
        log.info("神经网络训练完成");
    }
}